Senior Scientist, Applied Machine Learning & Generative AI, Pharma R&D

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

The Senior Scientist, Applied Machine Learning & Generative AI, Pharma R&D will execute analytical projects and capability builds to advance the Tempus drug R&D platform. This role involves performing complex computational analyses and developing algorithms for advancing cancer precision medicine for patients across the Tempus network. The ideal candidate will possess strong applied machine learning and generative AI skills, experience in applying various models to big data, and the ability to communicate complex findings to various stakeholders.

Description

• Data Expertise: Tempus has one of the largest multimodal patient datasets ever collected, providing a unique opportunity to work with extensive and diverse data. Become an expert in Tempus’ vast epidemiological, clinical, ‘omic and imaging data, along with the latest tools and techniques for their analysis and modeling. 
• Innovation: Drive continual improvement of the Tempus platform for pharmaceutical R&D by integrating client feedback, staying ahead of research and industry trends, and championing new opportunities, particularly in the realms of applied machine learning and generative AI.
• Teamwork: Work with Research, Engineering & Data Science teams across Tempus’ expansive data science community to develop and deliver innovative computational solutions.
• Drug R&D Expertise: Work with leading pharmaceutical companies. Gain proficiency in their strategies, drug modalities, and pipelines to identify where the Tempus platform can add value.
• Collaboration: Co-develop solutions with client science and clinical teams, and design, develop, and execute complex translational research projects leveraging the Tempus platform to advance their drug R&D programs.
• Scientific Communication: Skillfully navigates client interactions to extract and communicate the most impactful insights driving new R&D opportunities; effectively communicates complex technical results and methodologies to diverse external stakeholders.
• Personal development: Continuously immerse yourself in the latest industry trends, best practices, and advancements in machine learning and AI to revolutionize drug R&D

Qualifications

• Education and experience:
• Either
• PhD and an additional 2+ years of working experience
• Masters and additional 4+ years of working experience
• Combining:
• Quantitative and computational skills (e.g. Applied Machine Learning, Generative AI, Computational Biology, Biostatistics/Statistical Genetics, and/or Bioinformatics).
• Biological or medical knowledge (e.g. oncology, immunology, genomics, transcriptomics).
• Target, drug or diagnostic discovery, or clinical drug development.
• Technical/Scientific Skills:
• Proficient in R, Python, and SQL, and respective packages for computational biology and machine learning.
• Applicable knowledge of machine learning and statistical modeling.
• Strong understanding of the uses of artificial intelligence in molecular data analysis or drug discovery/development.
• Experience in integrative modeling of multi-modal clinical and omics data.
• Communication Skills: Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences. Comfort in a client-facing role.
• Motivated: Thrive in a fast-paced environment and willing to shift priorities seamlessly.

Preferred Skillsets/Background

• Strong peer-reviewed publication record.
• Strong understanding of cancer biology.
• Expertise in one or more of the following: in vitro data analysis and phenomics, network and systems biology, mechanistic modeling and simulation, knowledge analytics, deconvolution, and causal inference, integrative analysis of multi-modal data, real-world evidence, and survival analysis.
• Strong understanding of molecular data and artificial intelligence in drug discovery with experience in integrative modeling of multi-modal clinical and omics data.
• Previous experience working with large transcriptome and NGS data sets.
• Thrive in a fast-paced environment and willing to shift priorities seamlessly.
• Experience with R package development.
• Goal orientation, self-motivation, and drive to make a positive impact in healthcare.

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